The REINFORCE is an unbias gradient estimator for stochastic units, however, it is very complex to implement and often with high gradient variance during training [1]}. In this paper, we study the applicability of Gumbel-softmax [2]} [3]} in hard attention because Gumbel-softmax is an efficient way to estimate discrete units during training of neural networks. To mitigate the problem of sensitive temperature in Gumbel-softmax, we apply an adaptive temperature scheme [1]} in which the temperature value is also learnt from data. The experimental results verify that the adaptive temperature is a convenient way to avoid manual searching for the parameter. Additionally, we also test the deterministic soft attention [5]} [6]} and stochastic hard attention implemented by REINFORCE-like algorithms [7]} [8]} [5]} in action recognition. Combined with HM-RNN and two types of attention models, we systematically evaluate the proposed Hierarchical Multi-scale Attention Networks (HM-AN) for action recognition in videos, with improved results.
